Abstract: Systems and methods for querying large amounts of data are disclosed. Several different versions of a data feed are provided, ranging from a full set of data to various other versions that are smaller or faster to query (e.g., sampled versions, aggregations, sketches). A machine learning model is trained on features of input queries run against the various versions of the data feed and the corresponding results. The trained model is then applied to a new query to choose, automatically, which version of the data feed to apply the query against. That is, the system can select which version of the data feed to use when executing the given query, optimizing speed and/or compute costs while providing an appropriate level of accuracy for the given query.

Abstract: Improved security inspections for API traffic are disclosed. A data obfuscation process is applied to structured data in a request or response body to obfuscate the content while retaining the structural aspects thereof. The resulting sanitized version of the structured data is sent for analysis. For example a machine learning component is trained on such sanitized data to develop a signature or model that detects anomalous interactions with the API. The retained structure contains signals useful for pattern recognition and anomaly detection. The signature or model is preferably developed for a specific API endpoint. Then, a detection engine can be deployed to assess subsequent API traffic for the API endpoint, with such subsequent live traffic being similarly obfuscated by the system before being assessed. The teachings hereof can be used to block attacks or other malicious activities directed against API endpoints.

Abstract: Systems and methods for querying large amounts of data are disclosed. Several different versions of a data feed are provided, ranging from a full set of data to various other versions that are smaller or faster to query (e.g., sampled versions, aggregations, sketches) . . . . A machine learning model is trained on features of input queries run against the various versions of the data feed and the corresponding results. The trained model is then applied to a new query to choose, automatically, which version of the data feed to apply the query against. That is, the system can select which version of the data feed to use when executing the given query, optimizing speed and/or compute costs while providing an appropriate level of accuracy for the given query.

Abstract: A method, system and computer program product for facilitating risk mitigation of information security threats. Data obtained from at least one tracked data source is analyzed for identifying at least one event related to a threat, to be stored in a database comprising date and time of each event identified, enabling generation of threat timeline comprising temporally ordered sequence of each event related to respective threat identified. Features selected using correlation between features from threat timelines in the database and labeling assigned using records of threat usage incidents are extracted from events in threat timeline for the threat which the at least one event related thereto being identified and based thereon a dynamic score indicating an estimated level of risk posed by the threat is calculated using at least one machine learning model for predicting threat usage during a time window defined, enabling risk mitigation based on outputted indication thereof.

Abstract: A method, system and computer program product for facilitating risk mitigation of information security threats. Data obtained from at least one tracked data source is analyzed for identifying at least one event related to a threat, to be stored in a database comprising date and time of each event identified, enabling generation of threat timeline comprising temporally ordered sequence of each event related to respective threat identified. Features selected using correlation between features from threat timelines in the database and labeling assigned using records of threat usage incidents are extracted from events in threat timeline for the threat which the at least one event related thereto being identified and based thereon a dynamic score indicating an estimated level of risk posed by the threat is calculated using at least one machine learning model for predicting threat usage during a time window defined, enabling risk mitigation based on outputted indication thereof.